Overview

Dataset statistics

Number of variables18
Number of observations68187
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.9 MiB
Average record size in memory152.0 B

Variable types

Numeric9
Categorical9

Alerts

age is highly overall correlated with age_yearsHigh correlation
weight is highly overall correlated with bmi and 1 other fieldsHigh correlation
ap_hi is highly overall correlated with ap_lo and 2 other fieldsHigh correlation
ap_lo is highly overall correlated with ap_hi and 2 other fieldsHigh correlation
age_years is highly overall correlated with ageHigh correlation
bmi is highly overall correlated with weight and 1 other fieldsHigh correlation
New_BMI is highly overall correlated with weight and 1 other fieldsHigh correlation
bp_category is highly overall correlated with ap_hi and 2 other fieldsHigh correlation
bp_category_encoded is highly overall correlated with ap_hi and 2 other fieldsHigh correlation
gluc is highly imbalanced (52.2%)Imbalance
smoke is highly imbalanced (57.1%)Imbalance
alco is highly imbalanced (70.0%)Imbalance
id is uniformly distributedUniform
id has unique valuesUnique

Reproduction

Analysis started2023-10-25 12:34:44.452172
Analysis finished2023-10-25 12:34:51.952611
Duration7.5 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct68187
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49972.49
Minimum0
Maximum99999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-10-26T01:34:52.021927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4951.3
Q124989.5
median50009
Q374879.5
95-th percentile94936.1
Maximum99999
Range99999
Interquartile range (IQR)49890

Descriptive statistics

Standard deviation28853.618
Coefficient of variation (CV)0.57739003
Kurtosis-1.198473
Mean49972.49
Median Absolute Deviation (MAD)24951
Skewness-0.001482544
Sum3.4074742 × 109
Variance8.3253125 × 108
MonotonicityStrictly increasing
2023-10-26T01:34:52.106221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
66649 1
 
< 0.1%
66625 1
 
< 0.1%
66626 1
 
< 0.1%
66628 1
 
< 0.1%
66630 1
 
< 0.1%
66631 1
 
< 0.1%
66632 1
 
< 0.1%
66633 1
 
< 0.1%
66635 1
 
< 0.1%
Other values (68177) 68177
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
ValueCountFrequency (%)
99999 1
< 0.1%
99998 1
< 0.1%
99996 1
< 0.1%
99995 1
< 0.1%
99993 1
< 0.1%
99992 1
< 0.1%
99991 1
< 0.1%
99990 1
< 0.1%
99988 1
< 0.1%
99986 1
< 0.1%

age
Real number (ℝ)

HIGH CORRELATION 

Distinct8060
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19462.585
Minimum10798
Maximum23713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-10-26T01:34:52.179101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10798
5-th percentile15054.3
Q117656
median19700
Q321323
95-th percentile23256
Maximum23713
Range12915
Interquartile range (IQR)3667

Descriptive statistics

Standard deviation2468.3322
Coefficient of variation (CV)0.12682448
Kurtosis-0.82604311
Mean19462.585
Median Absolute Deviation (MAD)1713
Skewness-0.30476348
Sum1.3270953 × 109
Variance6092663.7
MonotonicityNot monotonic
2023-10-26T01:34:52.274622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19741 32
 
< 0.1%
18253 31
 
< 0.1%
21892 30
 
< 0.1%
18236 30
 
< 0.1%
18184 30
 
< 0.1%
19733 29
 
< 0.1%
20389 29
 
< 0.1%
20376 29
 
< 0.1%
20442 29
 
< 0.1%
19770 28
 
< 0.1%
Other values (8050) 67890
99.6%
ValueCountFrequency (%)
10798 1
 
< 0.1%
10859 1
 
< 0.1%
10878 1
 
< 0.1%
10964 1
 
< 0.1%
14275 1
 
< 0.1%
14277 1
 
< 0.1%
14282 1
 
< 0.1%
14284 1
 
< 0.1%
14287 1
 
< 0.1%
14291 3
< 0.1%
ValueCountFrequency (%)
23713 1
< 0.1%
23701 1
< 0.1%
23692 1
< 0.1%
23690 1
< 0.1%
23687 1
< 0.1%
23684 1
< 0.1%
23678 1
< 0.1%
23677 1
< 0.1%
23675 2
< 0.1%
23673 2
< 0.1%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
1
44414 
2
23773 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68187
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 44414
65.1%
2 23773
34.9%

Length

2023-10-26T01:34:52.342856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-26T01:34:52.406463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 44414
65.1%
2 23773
34.9%

Most occurring characters

ValueCountFrequency (%)
1 44414
65.1%
2 23773
34.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68187
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 44414
65.1%
2 23773
34.9%

Most occurring scripts

ValueCountFrequency (%)
Common 68187
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 44414
65.1%
2 23773
34.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68187
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 44414
65.1%
2 23773
34.9%

height
Real number (ℝ)

Distinct105
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.37604
Minimum55
Maximum250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-10-26T01:34:52.482264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum55
5-th percentile152
Q1159
median165
Q3170
95-th percentile178
Maximum250
Range195
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.1716715
Coefficient of variation (CV)0.049713276
Kurtosis7.6541922
Mean164.37604
Median Absolute Deviation (MAD)5
Skewness-0.60913976
Sum11208309
Variance66.776215
MonotonicityNot monotonic
2023-10-26T01:34:52.576093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165 5728
 
8.4%
160 4889
 
7.2%
170 4583
 
6.7%
168 4307
 
6.3%
164 3323
 
4.9%
158 3236
 
4.7%
162 3179
 
4.7%
169 2741
 
4.0%
156 2681
 
3.9%
167 2486
 
3.6%
Other values (95) 31034
45.5%
ValueCountFrequency (%)
55 1
 
< 0.1%
57 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
64 1
 
< 0.1%
65 2
< 0.1%
67 3
< 0.1%
68 2
< 0.1%
70 2
< 0.1%
71 1
 
< 0.1%
ValueCountFrequency (%)
250 1
 
< 0.1%
207 1
 
< 0.1%
198 14
< 0.1%
197 4
 
< 0.1%
196 6
< 0.1%
195 6
< 0.1%
194 2
 
< 0.1%
193 6
< 0.1%
192 12
< 0.1%
191 11
< 0.1%

weight
Real number (ℝ)

HIGH CORRELATION 

Distinct267
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.112623
Minimum35
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-10-26T01:34:52.661557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile55
Q165
median72
Q382
95-th percentile100
Maximum200
Range165
Interquartile range (IQR)17

Descriptive statistics

Standard deviation14.27153
Coefficient of variation (CV)0.19256545
Kurtosis2.5525326
Mean74.112623
Median Absolute Deviation (MAD)8
Skewness1.0160874
Sum5053517.4
Variance203.67658
MonotonicityNot monotonic
2023-10-26T01:34:52.738067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 3779
 
5.5%
70 3692
 
5.4%
68 2767
 
4.1%
75 2675
 
3.9%
60 2670
 
3.9%
80 2569
 
3.8%
72 2249
 
3.3%
69 2152
 
3.2%
78 2035
 
3.0%
74 1827
 
2.7%
Other values (257) 41772
61.3%
ValueCountFrequency (%)
35 2
 
< 0.1%
35.45 1
 
< 0.1%
36 5
 
< 0.1%
37 6
 
< 0.1%
38 7
 
< 0.1%
39 9
 
< 0.1%
40 41
0.1%
41 34
< 0.1%
42 48
0.1%
42.2 1
 
< 0.1%
ValueCountFrequency (%)
200 2
< 0.1%
183 1
 
< 0.1%
180 4
< 0.1%
178 3
< 0.1%
177 1
 
< 0.1%
172 1
 
< 0.1%
171 1
 
< 0.1%
170 3
< 0.1%
169 1
 
< 0.1%
168 3
< 0.1%

ap_hi
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.43746
Minimum90
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-10-26T01:34:52.812672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile100
Q1120
median120
Q3140
95-th percentile160
Maximum180
Range90
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.961509
Coefficient of variation (CV)0.12624035
Kurtosis0.76062622
Mean126.43746
Median Absolute Deviation (MAD)10
Skewness0.73997479
Sum8621391
Variance254.76978
MonotonicityNot monotonic
2023-10-26T01:34:52.905826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 27649
40.5%
140 9323
 
13.7%
130 8905
 
13.1%
110 8612
 
12.6%
150 4196
 
6.2%
160 2792
 
4.1%
100 2560
 
3.8%
90 928
 
1.4%
170 647
 
0.9%
180 602
 
0.9%
Other values (76) 1973
 
2.9%
ValueCountFrequency (%)
90 928
 
1.4%
93 1
 
< 0.1%
95 28
 
< 0.1%
96 2
 
< 0.1%
99 4
 
< 0.1%
100 2560
3.8%
101 4
 
< 0.1%
102 8
 
< 0.1%
103 8
 
< 0.1%
104 6
 
< 0.1%
ValueCountFrequency (%)
180 602
0.9%
179 4
 
< 0.1%
178 2
 
< 0.1%
177 2
 
< 0.1%
176 3
 
< 0.1%
175 14
 
< 0.1%
174 3
 
< 0.1%
173 2
 
< 0.1%
172 8
 
< 0.1%
171 8
 
< 0.1%

ap_lo
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.265564
Minimum60
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-10-26T01:34:52.979800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile70
Q180
median80
Q390
95-th percentile100
Maximum120
Range60
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.1433353
Coefficient of variation (CV)0.11251181
Kurtosis0.9324608
Mean81.265564
Median Absolute Deviation (MAD)0
Skewness0.23905336
Sum5541255
Variance83.60058
MonotonicityNot monotonic
2023-10-26T01:34:53.065383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 34719
50.9%
90 14236
20.9%
70 10206
 
15.0%
100 3978
 
5.8%
60 2654
 
3.9%
79 357
 
0.5%
110 338
 
0.5%
85 290
 
0.4%
75 209
 
0.3%
95 158
 
0.2%
Other values (48) 1042
 
1.5%
ValueCountFrequency (%)
60 2654
3.9%
61 5
 
< 0.1%
62 7
 
< 0.1%
63 7
 
< 0.1%
64 10
 
< 0.1%
65 78
 
0.1%
66 11
 
< 0.1%
67 19
 
< 0.1%
68 13
 
< 0.1%
69 98
 
0.1%
ValueCountFrequency (%)
120 134
 
0.2%
119 2
 
< 0.1%
115 7
 
< 0.1%
114 1
 
< 0.1%
113 3
 
< 0.1%
112 1
 
< 0.1%
111 1
 
< 0.1%
110 338
0.5%
109 6
 
< 0.1%
108 3
 
< 0.1%

cholesterol
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
1
51209 
2
9187 
3
7791 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68187
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row3
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 51209
75.1%
2 9187
 
13.5%
3 7791
 
11.4%

Length

2023-10-26T01:34:53.143395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-26T01:34:53.205698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 51209
75.1%
2 9187
 
13.5%
3 7791
 
11.4%

Most occurring characters

ValueCountFrequency (%)
1 51209
75.1%
2 9187
 
13.5%
3 7791
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68187
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 51209
75.1%
2 9187
 
13.5%
3 7791
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
Common 68187
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 51209
75.1%
2 9187
 
13.5%
3 7791
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68187
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 51209
75.1%
2 9187
 
13.5%
3 7791
 
11.4%

gluc
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
1
58012 
3
 
5179
2
 
4996

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68187
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 58012
85.1%
3 5179
 
7.6%
2 4996
 
7.3%

Length

2023-10-26T01:34:53.277325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-26T01:34:53.337526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 58012
85.1%
3 5179
 
7.6%
2 4996
 
7.3%

Most occurring characters

ValueCountFrequency (%)
1 58012
85.1%
3 5179
 
7.6%
2 4996
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68187
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 58012
85.1%
3 5179
 
7.6%
2 4996
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common 68187
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 58012
85.1%
3 5179
 
7.6%
2 4996
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68187
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 58012
85.1%
3 5179
 
7.6%
2 4996
 
7.3%

smoke
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
62209 
1
 
5978

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68187
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 62209
91.2%
1 5978
 
8.8%

Length

2023-10-26T01:34:53.414230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-26T01:34:53.487549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 62209
91.2%
1 5978
 
8.8%

Most occurring characters

ValueCountFrequency (%)
0 62209
91.2%
1 5978
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68187
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 62209
91.2%
1 5978
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
Common 68187
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 62209
91.2%
1 5978
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68187
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 62209
91.2%
1 5978
 
8.8%

alco
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
64564 
1
 
3623

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68187
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 64564
94.7%
1 3623
 
5.3%

Length

2023-10-26T01:34:53.559378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-26T01:34:53.619247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 64564
94.7%
1 3623
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 64564
94.7%
1 3623
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68187
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 64564
94.7%
1 3623
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common 68187
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 64564
94.7%
1 3623
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68187
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 64564
94.7%
1 3623
 
5.3%

active
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
1
54790 
0
13397 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68187
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 54790
80.4%
0 13397
 
19.6%

Length

2023-10-26T01:34:53.692841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-26T01:34:53.750787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 54790
80.4%
0 13397
 
19.6%

Most occurring characters

ValueCountFrequency (%)
1 54790
80.4%
0 13397
 
19.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68187
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 54790
80.4%
0 13397
 
19.6%

Most occurring scripts

ValueCountFrequency (%)
Common 68187
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 54790
80.4%
0 13397
 
19.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68187
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 54790
80.4%
0 13397
 
19.6%

cardio
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
34522 
1
33665 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68187
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 34522
50.6%
1 33665
49.4%

Length

2023-10-26T01:34:53.829868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-26T01:34:53.903387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 34522
50.6%
1 33665
49.4%

Most occurring characters

ValueCountFrequency (%)
0 34522
50.6%
1 33665
49.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68187
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 34522
50.6%
1 33665
49.4%

Most occurring scripts

ValueCountFrequency (%)
Common 68187
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 34522
50.6%
1 33665
49.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68187
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 34522
50.6%
1 33665
49.4%

age_years
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.823397
Minimum29
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-10-26T01:34:53.975578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile41
Q148
median53
Q358
95-th percentile63
Maximum64
Range35
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.7697955
Coefficient of variation (CV)0.12815903
Kurtosis-0.8213646
Mean52.823397
Median Absolute Deviation (MAD)5
Skewness-0.30352224
Sum3601869
Variance45.830132
MonotonicityNot monotonic
2023-10-26T01:34:54.050892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
55 3824
 
5.6%
53 3751
 
5.5%
57 3567
 
5.2%
54 3528
 
5.2%
56 3507
 
5.1%
59 3481
 
5.1%
49 3335
 
4.9%
58 3311
 
4.9%
51 3273
 
4.8%
52 3193
 
4.7%
Other values (18) 33417
49.0%
ValueCountFrequency (%)
29 3
 
< 0.1%
30 1
 
< 0.1%
39 1749
2.6%
40 1590
2.3%
41 1855
2.7%
42 1388
2.0%
43 1981
2.9%
44 1475
2.2%
45 2039
3.0%
46 1594
2.3%
ValueCountFrequency (%)
64 2121
3.1%
63 2651
3.9%
62 2134
3.1%
61 2647
3.9%
60 3127
4.6%
59 3481
5.1%
58 3311
4.9%
57 3567
5.2%
56 3507
5.1%
55 3824
5.6%

bmi
Real number (ℝ)

HIGH CORRELATION 

Distinct3739
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.514316
Minimum12.254473
Maximum298.66667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-10-26T01:34:54.128111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12.254473
5-th percentile20.936639
Q123.875115
median26.346494
Q330.116213
95-th percentile37.253645
Maximum298.66667
Range286.41219
Interquartile range (IQR)6.2410984

Descriptive statistics

Standard deviation6.0223563
Coefficient of variation (CV)0.21888083
Kurtosis231.26197
Mean27.514316
Median Absolute Deviation (MAD)2.9229366
Skewness7.8396139
Sum1876118.7
Variance36.268775
MonotonicityNot monotonic
2023-10-26T01:34:54.206481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.87511478 930
 
1.4%
23.4375 641
 
0.9%
24.22145329 485
 
0.7%
25.71166208 359
 
0.5%
22.03856749 354
 
0.5%
23.03004535 342
 
0.5%
24.8015873 325
 
0.5%
23.52941176 313
 
0.5%
24.97704316 284
 
0.4%
25.390625 279
 
0.4%
Other values (3729) 63875
93.7%
ValueCountFrequency (%)
12.25447288 1
< 0.1%
12.85583104 1
< 0.1%
13.52082207 1
< 0.1%
13.76 1
< 0.1%
14.47950008 1
< 0.1%
14.52737603 1
< 0.1%
14.57725948 1
< 0.1%
14.6092038 1
< 0.1%
14.69237833 1
< 0.1%
14.70113665 2
< 0.1%
ValueCountFrequency (%)
298.6666667 1
< 0.1%
278.125 1
< 0.1%
267.768595 1
< 0.1%
237.7686328 1
< 0.1%
191.6666667 1
< 0.1%
187.7500769 1
< 0.1%
180.6780742 1
< 0.1%
178.9627465 1
< 0.1%
178.2134106 1
< 0.1%
170.4142012 1
< 0.1%

bp_category
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
Hypertension Stage 1
39743 
Hypertension Stage 2
15935 
Normal
9409 
Elevated
 
3100

Length

Max length20
Median length20
Mean length17.522607
Min length6

Characters and Unicode

Total characters1194814
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHypertension Stage 1
2nd rowHypertension Stage 2
3rd rowHypertension Stage 1
4th rowHypertension Stage 2
5th rowNormal

Common Values

ValueCountFrequency (%)
Hypertension Stage 1 39743
58.3%
Hypertension Stage 2 15935
23.4%
Normal 9409
 
13.8%
Elevated 3100
 
4.5%

Length

2023-10-26T01:34:54.277815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-26T01:34:54.356382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
hypertension 55678
31.0%
stage 55678
31.0%
1 39743
22.1%
2 15935
 
8.9%
normal 9409
 
5.2%
elevated 3100
 
1.7%

Most occurring characters

ValueCountFrequency (%)
e 173234
14.5%
t 114456
 
9.6%
111356
 
9.3%
n 111356
 
9.3%
a 68187
 
5.7%
r 65087
 
5.4%
o 65087
 
5.4%
H 55678
 
4.7%
g 55678
 
4.7%
y 55678
 
4.7%
Other values (12) 319017
26.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 903915
75.7%
Uppercase Letter 123865
 
10.4%
Space Separator 111356
 
9.3%
Decimal Number 55678
 
4.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 173234
19.2%
t 114456
12.7%
n 111356
12.3%
a 68187
 
7.5%
r 65087
 
7.2%
o 65087
 
7.2%
g 55678
 
6.2%
y 55678
 
6.2%
i 55678
 
6.2%
s 55678
 
6.2%
Other values (5) 83796
9.3%
Uppercase Letter
ValueCountFrequency (%)
H 55678
45.0%
S 55678
45.0%
N 9409
 
7.6%
E 3100
 
2.5%
Decimal Number
ValueCountFrequency (%)
1 39743
71.4%
2 15935
28.6%
Space Separator
ValueCountFrequency (%)
111356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1027780
86.0%
Common 167034
 
14.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 173234
16.9%
t 114456
11.1%
n 111356
10.8%
a 68187
 
6.6%
r 65087
 
6.3%
o 65087
 
6.3%
H 55678
 
5.4%
g 55678
 
5.4%
y 55678
 
5.4%
S 55678
 
5.4%
Other values (9) 207661
20.2%
Common
ValueCountFrequency (%)
111356
66.7%
1 39743
 
23.8%
2 15935
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1194814
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 173234
14.5%
t 114456
 
9.6%
111356
 
9.3%
n 111356
 
9.3%
a 68187
 
5.7%
r 65087
 
5.4%
o 65087
 
5.4%
H 55678
 
4.7%
g 55678
 
4.7%
y 55678
 
4.7%
Other values (12) 319017
26.7%

bp_category_encoded
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
Hypertension Stage 1
39743 
Hypertension Stage 2
15935 
Normal
9409 
Elevated
 
3100

Length

Max length20
Median length20
Mean length17.522607
Min length6

Characters and Unicode

Total characters1194814
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHypertension Stage 1
2nd rowHypertension Stage 2
3rd rowHypertension Stage 1
4th rowHypertension Stage 2
5th rowNormal

Common Values

ValueCountFrequency (%)
Hypertension Stage 1 39743
58.3%
Hypertension Stage 2 15935
23.4%
Normal 9409
 
13.8%
Elevated 3100
 
4.5%

Length

2023-10-26T01:34:54.429102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-26T01:34:54.505353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
hypertension 55678
31.0%
stage 55678
31.0%
1 39743
22.1%
2 15935
 
8.9%
normal 9409
 
5.2%
elevated 3100
 
1.7%

Most occurring characters

ValueCountFrequency (%)
e 173234
14.5%
t 114456
 
9.6%
111356
 
9.3%
n 111356
 
9.3%
a 68187
 
5.7%
r 65087
 
5.4%
o 65087
 
5.4%
H 55678
 
4.7%
g 55678
 
4.7%
y 55678
 
4.7%
Other values (12) 319017
26.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 903915
75.7%
Uppercase Letter 123865
 
10.4%
Space Separator 111356
 
9.3%
Decimal Number 55678
 
4.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 173234
19.2%
t 114456
12.7%
n 111356
12.3%
a 68187
 
7.5%
r 65087
 
7.2%
o 65087
 
7.2%
g 55678
 
6.2%
y 55678
 
6.2%
i 55678
 
6.2%
s 55678
 
6.2%
Other values (5) 83796
9.3%
Uppercase Letter
ValueCountFrequency (%)
H 55678
45.0%
S 55678
45.0%
N 9409
 
7.6%
E 3100
 
2.5%
Decimal Number
ValueCountFrequency (%)
1 39743
71.4%
2 15935
28.6%
Space Separator
ValueCountFrequency (%)
111356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1027780
86.0%
Common 167034
 
14.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 173234
16.9%
t 114456
11.1%
n 111356
10.8%
a 68187
 
6.6%
r 65087
 
6.3%
o 65087
 
6.3%
H 55678
 
5.4%
g 55678
 
5.4%
y 55678
 
5.4%
S 55678
 
5.4%
Other values (9) 207661
20.2%
Common
ValueCountFrequency (%)
111356
66.7%
1 39743
 
23.8%
2 15935
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1194814
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 173234
14.5%
t 114456
 
9.6%
111356
 
9.3%
n 111356
 
9.3%
a 68187
 
5.7%
r 65087
 
5.4%
o 65087
 
5.4%
H 55678
 
4.7%
g 55678
 
4.7%
y 55678
 
4.7%
Other values (12) 319017
26.7%

New_BMI
Real number (ℝ)

HIGH CORRELATION 

Distinct3740
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.514316
Minimum12.254473
Maximum298.66667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-10-26T01:34:54.579968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12.254473
5-th percentile20.936639
Q123.875115
median26.346494
Q330.116213
95-th percentile37.253645
Maximum298.66667
Range286.41219
Interquartile range (IQR)6.2410984

Descriptive statistics

Standard deviation6.0223563
Coefficient of variation (CV)0.21888083
Kurtosis231.26197
Mean27.514316
Median Absolute Deviation (MAD)2.9229366
Skewness7.8396139
Sum1876118.7
Variance36.268775
MonotonicityNot monotonic
2023-10-26T01:34:54.673324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.87511478 930
 
1.4%
23.4375 641
 
0.9%
24.22145329 485
 
0.7%
25.71166208 359
 
0.5%
22.03856749 354
 
0.5%
23.03004535 342
 
0.5%
24.8015873 325
 
0.5%
23.52941176 313
 
0.5%
24.97704316 284
 
0.4%
25.390625 279
 
0.4%
Other values (3730) 63875
93.7%
ValueCountFrequency (%)
12.25447288 1
< 0.1%
12.85583104 1
< 0.1%
13.52082207 1
< 0.1%
13.76 1
< 0.1%
14.47950008 1
< 0.1%
14.52737603 1
< 0.1%
14.57725948 1
< 0.1%
14.6092038 1
< 0.1%
14.69237833 1
< 0.1%
14.70113665 2
< 0.1%
ValueCountFrequency (%)
298.6666667 1
< 0.1%
278.125 1
< 0.1%
267.768595 1
< 0.1%
237.7686328 1
< 0.1%
191.6666667 1
< 0.1%
187.7500769 1
< 0.1%
180.6780742 1
< 0.1%
178.9627465 1
< 0.1%
178.2134106 1
< 0.1%
170.4142012 1
< 0.1%

Interactions

2023-10-26T01:34:50.927904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:46.102819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:46.726101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:47.325620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:47.928933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:48.529076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:49.125623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:49.730390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:50.326017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:50.992228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:46.181765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:46.796517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:47.392418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:47.995354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:48.592953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:49.192809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:49.794972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:50.396546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:51.062325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:46.249637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:46.862997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:47.462091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:48.059791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:48.662085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:49.259908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:49.859560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:50.459509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:51.126054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:46.331004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:46.927916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:47.525340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:48.126527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:48.730698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:49.325997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:49.926252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:50.527589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:51.194755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:46.392692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:46.996092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:47.595255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:48.195452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:48.795576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:49.393338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:49.993762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:50.595856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:51.408728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:46.463536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:47.062585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:47.662083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:48.261327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:48.863454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:49.462354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:50.059812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:50.660208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:51.479810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:46.529436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:47.127888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:47.725024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:48.326503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:48.926143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:49.525783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:50.131445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:50.726496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:51.546543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:46.592253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:47.192665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:47.795147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:48.394943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:48.992596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:49.597067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:50.194434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:50.795345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:51.614133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:46.663828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:47.261100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:47.859510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:48.460206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:49.062205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:49.658488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:50.263165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-26T01:34:50.863316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-26T01:34:54.735854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idageheightweightap_hiap_loage_yearsbmiNew_BMIgendercholesterolglucsmokealcoactivecardiobp_categorybp_category_encoded
id1.0000.003-0.002-0.0020.003-0.0010.003-0.001-0.0010.0130.0060.0000.0040.0000.0060.0060.0060.006
age0.0031.000-0.0820.0620.2220.1570.9990.1080.1080.0520.1130.0710.0480.0280.0140.2400.1120.112
height-0.002-0.0821.0000.3140.0210.031-0.084-0.183-0.1830.4150.0310.0120.1690.0890.0140.0170.0370.037
weight-0.0020.0620.3141.0000.2760.2490.0630.8480.8480.1700.0990.0850.0700.0660.0240.1710.1390.139
ap_hi0.0030.2220.0210.2761.0000.7410.2230.2780.2780.0860.1740.0890.0310.0380.0210.4630.6780.678
ap_lo-0.0010.1570.0310.2490.7411.0000.1580.2440.2440.0720.1310.0650.0250.0450.0090.3650.7230.723
age_years0.0030.999-0.0840.0630.2230.1581.0000.1100.1100.0510.1120.0700.0480.0290.0150.2400.1120.112
bmi-0.0010.108-0.1830.8480.2780.2440.1101.0001.0000.0650.0410.0400.0150.0000.0100.0530.0400.040
New_BMI-0.0010.108-0.1830.8480.2780.2440.1101.0001.0000.0650.0410.0400.0150.0000.0100.0530.0400.040
gender0.0130.0520.4150.1700.0860.0720.0510.0650.0651.0000.0370.0210.3380.1710.0030.0050.0800.080
cholesterol0.0060.1130.0310.0990.1740.1310.1120.0410.0410.0371.0000.3930.0240.0430.0120.2210.1220.122
gluc0.0000.0710.0120.0850.0890.0650.0700.0400.0400.0210.3931.0000.0190.0290.0110.0910.0630.063
smoke0.0040.0480.1690.0700.0310.0250.0480.0150.0150.3380.0240.0191.0000.3380.0250.0160.0200.020
alco0.0000.0280.0890.0660.0380.0450.0290.0000.0000.1710.0430.0290.3381.0000.0240.0080.0300.030
active0.0060.0140.0140.0240.0210.0090.0150.0100.0100.0030.0120.0110.0250.0241.0000.0380.0140.014
cardio0.0060.2400.0170.1710.4630.3650.2400.0530.0530.0050.2210.0910.0160.0080.0381.0000.3730.373
bp_category0.0060.1120.0370.1390.6780.7230.1120.0400.0400.0800.1220.0630.0200.0300.0140.3731.0001.000
bp_category_encoded0.0060.1120.0370.1390.6780.7230.1120.0400.0400.0800.1220.0630.0200.0300.0140.3731.0001.000

Missing values

2023-10-26T01:34:51.701789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-26T01:34:51.852255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idagegenderheightweightap_hiap_locholesterolglucsmokealcoactivecardioage_yearsbmibp_categorybp_category_encodedNew_BMI
0018393216862.0110801100105021.967120Hypertension Stage 1Hypertension Stage 121.967120
1120228115685.0140903100115534.927679Hypertension Stage 2Hypertension Stage 234.927679
2218857116564.0130703100015123.507805Hypertension Stage 1Hypertension Stage 123.507805
3317623216982.01501001100114828.710479Hypertension Stage 2Hypertension Stage 228.710479
4417474115656.0100601100004723.011177NormalNormal23.011177
5821914115167.0120802200006029.384676Hypertension Stage 1Hypertension Stage 129.384676
6922113115793.0130803100106037.729725Hypertension Stage 1Hypertension Stage 137.729725
71222584217895.0130903300116129.983588Hypertension Stage 1Hypertension Stage 129.983588
81317668115871.0110701100104828.440955NormalNormal28.440955
91419834116468.0110601100005425.282570NormalNormal25.282570
idagegenderheightweightap_hiap_locholesterolglucsmokealcoactivecardioage_yearsbmibp_categorybp_category_encodedNew_BMI
681959998615094116872.0110701100114125.510204NormalNormal25.510204
681969998820609115972.0130902200105628.479886Hypertension Stage 1Hypertension Stage 128.479886
681979999018792116156.0170901100115121.604105Hypertension Stage 2Hypertension Stage 221.604105
681989999119699117270.0130901100115323.661439Hypertension Stage 1Hypertension Stage 123.661439
681999999221074116580.0150801100115729.384757Hypertension Stage 1Hypertension Stage 129.384757
682009999319240216876.0120801110105226.927438Hypertension Stage 1Hypertension Stage 126.927438
6820199995226011158126.0140902200116150.472681Hypertension Stage 2Hypertension Stage 250.472681
6820299996190662183105.0180903101015231.353579Hypertension Stage 2Hypertension Stage 231.353579
682039999822431116372.0135801200016127.099251Hypertension Stage 1Hypertension Stage 127.099251
682049999920540117072.0120802100105624.913495Hypertension Stage 1Hypertension Stage 124.913495